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README.md
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# Aegolius Acadicus
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# Prompting
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## Prompt Template for alpaca style
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```
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### Instruction:
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<prompt> (without the <>)
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### Response:
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```
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## Sample Code
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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torch.set_default_device("cuda")
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model = AutoModelForCausalLM.from_pretrained("ibivibiv/aegolius-acadicus-30b", torch_dtype="auto", device_config='auto')
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tokenizer = AutoTokenizer.from_pretrained("ibivibiv/aegolius-acadicus-30b")
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inputs = tokenizer("### Instruction: Who would when in an arm wrestling match between Abraham Lincoln and Chuck Norris?\n### Response:\n", return_tensors="pt", return_attention_mask=False)
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outputs = model.generate(**inputs, max_length=200)
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text = tokenizer.batch_decode(outputs)[0]
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print(text)
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```
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# Model Details
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* **Trained by**: [ibivibiv](https://huggingface.co/ibivibiv)
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* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
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* **Model type:** **aegolius-acadicus-30b** is an auto-regressive language model moe from Llama 2 transformer architecture models and mistral models.
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* **Language(s)**: English
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* **Purpose**: This model is an attempt at an moe model to cover multiple disciplines using finetuned llama 2 and mistral models as base models.
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# Benchmark Scores
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pending
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## Citations
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```
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@misc{open-llm-leaderboard,
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author = {Edward Beeching and Clémentine Fourrier and Nathan Habib and Sheon Han and Nathan Lambert and Nazneen Rajani and Omar Sanseviero and Lewis Tunstall and Thomas Wolf},
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title = {Open LLM Leaderboard},
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year = {2023},
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publisher = {Hugging Face},
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howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}"
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}
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```
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```
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@software{eval-harness,
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author = {Gao, Leo and
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Tow, Jonathan and
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Biderman, Stella and
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Black, Sid and
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DiPofi, Anthony and
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Foster, Charles and
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Golding, Laurence and
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Hsu, Jeffrey and
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McDonell, Kyle and
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Muennighoff, Niklas and
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Phang, Jason and
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Reynolds, Laria and
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Tang, Eric and
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Thite, Anish and
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Wang, Ben and
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Wang, Kevin and
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Zou, Andy},
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title = {A framework for few-shot language model evaluation},
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month = sep,
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year = 2021,
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publisher = {Zenodo},
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version = {v0.0.1},
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doi = {10.5281/zenodo.5371628},
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url = {https://doi.org/10.5281/zenodo.5371628}
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}
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```
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```
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@misc{clark2018think,
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title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
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author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
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year={2018},
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eprint={1803.05457},
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archivePrefix={arXiv},
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primaryClass={cs.AI}
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}
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```
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```
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@misc{zellers2019hellaswag,
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title={HellaSwag: Can a Machine Really Finish Your Sentence?},
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author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi},
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year={2019},
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eprint={1905.07830},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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```
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@misc{hendrycks2021measuring,
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title={Measuring Massive Multitask Language Understanding},
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author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
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year={2021},
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eprint={2009.03300},
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archivePrefix={arXiv},
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primaryClass={cs.CY}
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}
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```
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```
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@misc{lin2022truthfulqa,
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title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
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author={Stephanie Lin and Jacob Hilton and Owain Evans},
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year={2022},
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eprint={2109.07958},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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```
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@misc{DBLP:journals/corr/abs-1907-10641,
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title={{WINOGRANDE:} An Adversarial Winograd Schema Challenge at Scale},
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author={Keisuke Sakaguchi and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
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year={2019},
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eprint={1907.10641},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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```
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@misc{DBLP:journals/corr/abs-2110-14168,
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title={Training Verifiers to Solve Math Word Problems},
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author={Karl Cobbe and
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Vineet Kosaraju and
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Mohammad Bavarian and
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Mark Chen and
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Heewoo Jun and
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Lukasz Kaiser and
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Matthias Plappert and
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Jerry Tworek and
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Jacob Hilton and
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Reiichiro Nakano and
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Christopher Hesse and
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John Schulman},
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year={2021},
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eprint={2110.14168},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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